Machine Learning-Based Missing Value Imputation Method for Clinical Datasets

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)

Abstract

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.

Keywords

Cardiovascular FURIA Fuzzy rules J48 K-Mean Missing value 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HullHullUK
  2. 2.Department of Computer ScienceEastern University DhakaDhakaBangladesh

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